Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations913595
Missing cells1465
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory175.1 MiB
Average record size in memory201.0 B

Variable types

Numeric14
DateTime3
Categorical3

Alerts

breadth is highly overall correlated with draught and 1 other fieldsHigh correlation
course_over_ground is highly overall correlated with true_headingHigh correlation
destination is highly overall correlated with end_latitude and 4 other fieldsHigh correlation
draught is highly overall correlated with breadth and 1 other fieldsHigh correlation
end_latitude is highly overall correlated with destination and 7 other fieldsHigh correlation
end_longitude is highly overall correlated with destination and 7 other fieldsHigh correlation
end_port is highly overall correlated with destination and 7 other fieldsHigh correlation
latitude is highly overall correlated with end_latitude and 6 other fieldsHigh correlation
length is highly overall correlated with breadth and 1 other fieldsHigh correlation
longitude is highly overall correlated with end_latitude and 6 other fieldsHigh correlation
start_latitude is highly overall correlated with end_latitude and 6 other fieldsHigh correlation
start_longitude is highly overall correlated with destination and 7 other fieldsHigh correlation
start_port is highly overall correlated with destination and 7 other fieldsHigh correlation
true_heading is highly overall correlated with course_over_groundHigh correlation
destination is highly imbalanced (55.3%) Imbalance

Reproduction

Analysis started2025-06-10 22:05:04.508589
Analysis finished2025-06-10 22:05:56.347438
Duration51.84 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

trip_id
Real number (ℝ)

Distinct1126
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125611.9
Minimum5944
Maximum2278147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:56.429917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5944
5-th percentile55365
Q1473272
median1170717
Q31669988
95-th percentile2160044
Maximum2278147
Range2272203
Interquartile range (IQR)1196716

Descriptive statistics

Standard deviation673814.36
Coefficient of variation (CV)0.59862048
Kurtosis-1.2739622
Mean1125611.9
Median Absolute Deviation (MAD)546619
Skewness-0.039517174
Sum1.0283534 × 1012
Variance4.5402579 × 1011
MonotonicityNot monotonic
2025-06-11T00:05:56.571680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2183472 2609
 
0.3%
1669988 2362
 
0.3%
265994 2085
 
0.2%
1778056 2068
 
0.2%
2201111 2059
 
0.2%
1993462 2053
 
0.2%
1019076 2036
 
0.2%
2183480 2026
 
0.2%
624032 1994
 
0.2%
1677413 1989
 
0.2%
Other values (1116) 892314
97.7%
ValueCountFrequency (%)
5944 1237
0.1%
10257 577
 
0.1%
19002 41
 
< 0.1%
19585 1646
0.2%
23834 1241
0.1%
24805 595
 
0.1%
25124 659
0.1%
28257 1280
0.1%
29139 1247
0.1%
29152 1296
0.1%
ValueCountFrequency (%)
2278147 518
0.1%
2278140 588
0.1%
2278125 528
0.1%
2278114 574
0.1%
2278113 574
0.1%
2278085 522
0.1%
2278084 522
0.1%
2278083 522
0.1%
2278070 494
0.1%
2278069 494
0.1%

start_latitude
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.033007
Minimum53.33
Maximum54.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:56.690749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum53.33
5-th percentile53.52
Q153.58
median54.36
Q354.36
95-th percentile54.36
Maximum54.54
Range1.21
Interquartile range (IQR)0.78

Descriptive statistics

Standard deviation0.39140852
Coefficient of variation (CV)0.0072438781
Kurtosis-1.8378472
Mean54.033007
Median Absolute Deviation (MAD)0
Skewness-0.3615352
Sum49364285
Variance0.15320063
MonotonicityNot monotonic
2025-06-11T00:05:56.812968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
54.36 525478
57.5%
53.58 168718
 
18.5%
53.59 56719
 
6.2%
53.51 34008
 
3.7%
53.57 26593
 
2.9%
53.53 23437
 
2.6%
53.6 21983
 
2.4%
53.52 10380
 
1.1%
53.55 5686
 
0.6%
53.56 5650
 
0.6%
Other values (18) 34943
 
3.8%
ValueCountFrequency (%)
53.33 3169
 
0.3%
53.34 2535
 
0.3%
53.5 1285
 
0.1%
53.51 34008
3.7%
53.52 10380
 
1.1%
53.53 23437
2.6%
53.54 1288
 
0.1%
53.55 5686
 
0.6%
53.56 5650
 
0.6%
53.57 26593
2.9%
ValueCountFrequency (%)
54.54 3667
 
0.4%
54.49 552
 
0.1%
54.37 1257
 
0.1%
54.36 525478
57.5%
54.33 3161
 
0.3%
54.31 1158
 
0.1%
53.75 976
 
0.1%
53.74 530
 
0.1%
53.67 2150
 
0.2%
53.66 411
 
< 0.1%

start_longitude
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4652193
Minimum8.14
Maximum10.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:56.936662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8.14
5-th percentile8.5
Q18.52
median10.14
Q310.14
95-th percentile10.14
Maximum10.34
Range2.2
Interquartile range (IQR)1.62

Descriptive statistics

Standard deviation0.80564026
Coefficient of variation (CV)0.085115858
Kurtosis-1.8490887
Mean9.4652193
Median Absolute Deviation (MAD)0
Skewness-0.36017706
Sum8647377
Variance0.64905624
MonotonicityNot monotonic
2025-06-11T00:05:57.054207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10.14 498283
54.5%
8.52 160444
 
17.6%
8.53 78601
 
8.6%
8.51 36432
 
4.0%
8.57 31343
 
3.4%
10.13 24011
 
2.6%
8.54 15226
 
1.7%
8.5 11982
 
1.3%
8.15 10964
 
1.2%
8.49 9062
 
1.0%
Other values (24) 37247
 
4.1%
ValueCountFrequency (%)
8.14 485
 
0.1%
8.15 10964
1.2%
8.16 1297
 
0.1%
8.19 976
 
0.1%
8.22 530
 
0.1%
8.36 1078
 
0.1%
8.37 1072
 
0.1%
8.38 411
 
< 0.1%
8.39 984
 
0.1%
8.4 523
 
0.1%
ValueCountFrequency (%)
10.34 552
 
0.1%
10.29 3667
 
0.4%
10.18 3188
 
0.3%
10.17 2933
 
0.3%
10.16 1485
 
0.2%
10.15 1154
 
0.1%
10.14 498283
54.5%
10.13 24011
 
2.6%
8.57 31343
 
3.4%
8.56 3146
 
0.3%
Distinct953
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2016-01-13 06:03:00+00:00
Maximum2017-05-26 19:44:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-11T00:05:57.182360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:57.310915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

end_latitude
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.084077
Minimum53.47
Maximum54.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:57.428000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum53.47
5-th percentile53.5
Q153.53
median54.38
Q354.53
95-th percentile54.54
Maximum54.64
Range1.17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.47536535
Coefficient of variation (CV)0.0087893772
Kurtosis-1.8529172
Mean54.084077
Median Absolute Deviation (MAD)0.16
Skewness-0.31327499
Sum49410942
Variance0.22597222
MonotonicityNot monotonic
2025-06-11T00:05:57.552501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
54.54 217507
23.8%
53.53 194398
21.3%
53.5 87533
9.6%
54.38 85200
 
9.3%
54.52 54379
 
6.0%
54.53 40516
 
4.4%
53.54 37790
 
4.1%
54.44 33365
 
3.7%
53.52 31820
 
3.5%
54.43 24028
 
2.6%
Other values (19) 107059
11.7%
ValueCountFrequency (%)
53.47 1290
 
0.1%
53.48 677
 
0.1%
53.49 9439
 
1.0%
53.5 87533
9.6%
53.51 10829
 
1.2%
53.52 31820
 
3.5%
53.53 194398
21.3%
53.54 37790
 
4.1%
53.56 678
 
0.1%
53.61 2018
 
0.2%
ValueCountFrequency (%)
54.64 1646
 
0.2%
54.59 740
 
0.1%
54.54 217507
23.8%
54.53 40516
 
4.4%
54.52 54379
 
6.0%
54.51 5163
 
0.6%
54.5 522
 
0.1%
54.47 1280
 
0.1%
54.46 7687
 
0.8%
54.45 8797
 
1.0%

end_longitude
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.995004
Minimum9.5
Maximum18.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:57.683100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile9.9
Q19.93
median18.5
Q318.6
95-th percentile18.71
Maximum18.92
Range9.42
Interquartile range (IQR)8.67

Descriptive statistics

Standard deviation4.2753309
Coefficient of variation (CV)0.28511702
Kurtosis-1.8773597
Mean14.995004
Median Absolute Deviation (MAD)0.17
Skewness-0.34812911
Sum13699361
Variance18.278454
MonotonicityNot monotonic
2025-06-11T00:05:57.816614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
18.51 115647
12.7%
18.5 101860
 
11.1%
9.91 100740
 
11.0%
9.93 76722
 
8.4%
9.9 60305
 
6.6%
18.65 45248
 
5.0%
9.95 44441
 
4.9%
18.66 44309
 
4.8%
18.67 37754
 
4.1%
9.82 27616
 
3.0%
Other values (37) 258953
28.3%
ValueCountFrequency (%)
9.5 1189
 
0.1%
9.51 661
 
0.1%
9.54 1196
 
0.1%
9.55 178
 
< 0.1%
9.56 644
 
0.1%
9.73 678
 
0.1%
9.82 27616
3.0%
9.83 6963
 
0.8%
9.88 1221
 
0.1%
9.9 60305
6.6%
ValueCountFrequency (%)
18.92 1646
 
0.2%
18.88 740
 
0.1%
18.83 7385
 
0.8%
18.82 11996
1.3%
18.81 4884
 
0.5%
18.75 1500
 
0.2%
18.72 3774
 
0.4%
18.71 27107
3.0%
18.7 7897
 
0.9%
18.68 3368
 
0.4%
Distinct943
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2016-01-13 14:36:00+00:00
Maximum2017-05-27 22:11:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-11T00:05:57.947266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:58.177713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

start_port
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.5 KiB
KIEL
535273 
BREMERHAVEN
378322 

Length

Max length11
Median length4
Mean length6.8987177
Min length4

Characters and Unicode

Total characters6302634
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBREMERHAVEN
2nd rowBREMERHAVEN
3rd rowBREMERHAVEN
4th rowBREMERHAVEN
5th rowBREMERHAVEN

Common Values

ValueCountFrequency (%)
KIEL 535273
58.6%
BREMERHAVEN 378322
41.4%

Length

2025-06-11T00:05:58.302536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:05:58.402132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
kiel 535273
58.6%
bremerhaven 378322
41.4%

Most occurring characters

ValueCountFrequency (%)
E 1670239
26.5%
R 756644
12.0%
K 535273
 
8.5%
I 535273
 
8.5%
L 535273
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6302634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1670239
26.5%
R 756644
12.0%
K 535273
 
8.5%
I 535273
 
8.5%
L 535273
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6302634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1670239
26.5%
R 756644
12.0%
K 535273
 
8.5%
I 535273
 
8.5%
L 535273
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6302634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1670239
26.5%
R 756644
12.0%
K 535273
 
8.5%
I 535273
 
8.5%
L 535273
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

end_port
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.5 KiB
GDYNIA
535273 
HAMBURG
378322 

Length

Max length7
Median length6
Mean length6.4141025
Min length6

Characters and Unicode

Total characters5859892
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHAMBURG
2nd rowHAMBURG
3rd rowHAMBURG
4th rowHAMBURG
5th rowHAMBURG

Common Values

ValueCountFrequency (%)
GDYNIA 535273
58.6%
HAMBURG 378322
41.4%

Length

2025-06-11T00:05:58.503500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:05:58.593254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gdynia 535273
58.6%
hamburg 378322
41.4%

Most occurring characters

ValueCountFrequency (%)
G 913595
15.6%
A 913595
15.6%
D 535273
9.1%
Y 535273
9.1%
N 535273
9.1%
I 535273
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5859892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 913595
15.6%
A 913595
15.6%
D 535273
9.1%
Y 535273
9.1%
N 535273
9.1%
I 535273
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5859892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 913595
15.6%
A 913595
15.6%
D 535273
9.1%
Y 535273
9.1%
N 535273
9.1%
I 535273
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5859892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 913595
15.6%
A 913595
15.6%
D 535273
9.1%
Y 535273
9.1%
N 535273
9.1%
I 535273
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%
Distinct414192
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2016-01-13 06:03:00+00:00
Maximum2017-05-27 22:11:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-11T00:05:58.699951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:58.826606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ship_type
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing1397
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean72.273954
Minimum69
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:58.930822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile70
Q170
median71
Q371
95-th percentile80
Maximum89
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5283781
Coefficient of variation (CV)0.048819498
Kurtosis0.87815848
Mean72.273954
Median Absolute Deviation (MAD)1
Skewness1.5950954
Sum65928156
Variance12.449452
MonotonicityNot monotonic
2025-06-11T00:05:59.039207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
71 409205
44.8%
70 311891
34.1%
79 123826
 
13.6%
81 34540
 
3.8%
72 12889
 
1.4%
80 11035
 
1.2%
73 3629
 
0.4%
74 3441
 
0.4%
69 1158
 
0.1%
89 584
 
0.1%
(Missing) 1397
 
0.2%
ValueCountFrequency (%)
69 1158
 
0.1%
70 311891
34.1%
71 409205
44.8%
72 12889
 
1.4%
73 3629
 
0.4%
74 3441
 
0.4%
79 123826
 
13.6%
80 11035
 
1.2%
81 34540
 
3.8%
89 584
 
0.1%
ValueCountFrequency (%)
89 584
 
0.1%
81 34540
 
3.8%
80 11035
 
1.2%
79 123826
 
13.6%
74 3441
 
0.4%
73 3629
 
0.4%
72 12889
 
1.4%
71 409205
44.8%
70 311891
34.1%
69 1158
 
0.1%

length
Real number (ℝ)

High correlation 

Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58654
Minimum45
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:59.168185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile74
Q189
median134
Q3154
95-th percentile171
Maximum399
Range354
Interquartile range (IQR)65

Descriptive statistics

Standard deviation42.463217
Coefficient of variation (CV)0.33023064
Kurtosis6.8184036
Mean128.58654
Median Absolute Deviation (MAD)28
Skewness1.4993322
Sum1.1747602 × 108
Variance1803.1248
MonotonicityNot monotonic
2025-06-11T00:05:59.310313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 89298
 
9.8%
134 77596
 
8.5%
88 61305
 
6.7%
155 58877
 
6.4%
125 47108
 
5.2%
168 40709
 
4.5%
82 34548
 
3.8%
79 34121
 
3.7%
154 32709
 
3.6%
68 31700
 
3.5%
Other values (88) 405624
44.4%
ValueCountFrequency (%)
45 1771
 
0.2%
65 1648
 
0.2%
66 1522
 
0.2%
68 31700
3.5%
70 5482
 
0.6%
74 9892
 
1.1%
75 10113
 
1.1%
79 34121
3.7%
80 1617
 
0.2%
81 8660
 
0.9%
ValueCountFrequency (%)
399 1716
0.2%
397 1153
 
0.1%
396 660
 
0.1%
300 1142
 
0.1%
299 537
 
0.1%
295 1252
 
0.1%
294 2171
0.2%
293 1214
 
0.1%
277 1629
0.2%
275 3297
0.4%

breadth
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.291348
Minimum8
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:59.445221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q113
median22
Q324
95-th percentile27
Maximum60
Range52
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.1613635
Coefficient of variation (CV)0.30364486
Kurtosis5.1807798
Mean20.291348
Median Absolute Deviation (MAD)3
Skewness0.92568828
Sum18538074
Variance37.9624
MonotonicityNot monotonic
2025-06-11T00:05:59.572327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
22 138472
15.2%
23 111574
12.2%
12 106192
11.6%
24 101595
11.1%
13 95877
10.5%
25 67310
7.4%
26 50019
 
5.5%
18 42465
 
4.6%
19 38486
 
4.2%
27 26464
 
2.9%
Other values (24) 135141
14.8%
ValueCountFrequency (%)
8 1771
 
0.2%
9 637
 
0.1%
10 3532
 
0.4%
11 24132
 
2.6%
12 106192
11.6%
13 95877
10.5%
14 13278
 
1.5%
15 14257
 
1.6%
16 12904
 
1.4%
17 1662
 
0.2%
ValueCountFrequency (%)
60 1716
 
0.2%
59 660
 
0.1%
56 1153
 
0.1%
48 1142
 
0.1%
42 1071
 
0.1%
40 3919
 
0.4%
36 652
 
0.1%
35 564
 
0.1%
33 681
 
0.1%
32 13768
1.5%

draught
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4860624
Minimum0.1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:59.698803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile2.8
Q14.6
median7.2
Q38.3
95-th percentile9.3
Maximum14
Range13.9
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.187501
Coefficient of variation (CV)0.33726179
Kurtosis-1.0358573
Mean6.4860624
Median Absolute Deviation (MAD)1.6
Skewness-0.29239943
Sum5925634.2
Variance4.7851608
MonotonicityNot monotonic
2025-06-11T00:05:59.839061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 46318
 
5.1%
3.2 31677
 
3.5%
8.3 24085
 
2.6%
7.9 17477
 
1.9%
7.7 17073
 
1.9%
8.4 15522
 
1.7%
8.1 15030
 
1.6%
8 14613
 
1.6%
3.8 14105
 
1.5%
9.2 14024
 
1.5%
Other values (238) 703671
77.0%
ValueCountFrequency (%)
0.1 637
 
0.1%
1.93 1771
 
0.2%
2.3 1607
 
0.2%
2.4 1828
 
0.2%
2.5 1373
 
0.2%
2.6 7279
 
0.8%
2.63 41
 
< 0.1%
2.7 1782
 
0.2%
2.8 46318
5.1%
2.9 471
 
0.1%
ValueCountFrequency (%)
14 15
 
< 0.1%
13.23 544
0.1%
11.7 645
0.1%
11.54 1071
0.1%
11.45 551
0.1%
11.4 1223
0.1%
11.24 691
0.1%
11.1 627
0.1%
11 368
 
< 0.1%
10.98 211
 
< 0.1%

latitude
Real number (ℝ)

High correlation 

Distinct273
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.287471
Minimum53.33
Maximum56.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:05:59.977800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum53.33
5-th percentile53.56
Q153.85
median54.47
Q354.65
95-th percentile54.89
Maximum56.34
Range3.01
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.45826776
Coefficient of variation (CV)0.0084415014
Kurtosis-1.0504964
Mean54.287471
Median Absolute Deviation (MAD)0.34
Skewness-0.13312877
Sum49596762
Variance0.21000934
MonotonicityNot monotonic
2025-06-11T00:06:00.111171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.87 28797
 
3.2%
54.53 23462
 
2.6%
54.41 20333
 
2.2%
53.84 20044
 
2.2%
53.98 19631
 
2.1%
54.56 17212
 
1.9%
53.54 17009
 
1.9%
53.86 16700
 
1.8%
54.74 16683
 
1.8%
54.57 16667
 
1.8%
Other values (263) 717057
78.5%
ValueCountFrequency (%)
53.33 13
 
< 0.1%
53.34 40
< 0.1%
53.35 29
< 0.1%
53.36 26
< 0.1%
53.37 25
< 0.1%
53.38 27
< 0.1%
53.39 26
< 0.1%
53.4 23
< 0.1%
53.41 24
< 0.1%
53.42 26
< 0.1%
ValueCountFrequency (%)
56.34 2
 
< 0.1%
56.33 4
 
< 0.1%
56.32 4
 
< 0.1%
56.31 3
 
< 0.1%
56.3 3
 
< 0.1%
56.29 12
< 0.1%
56.28 8
< 0.1%
56.27 5
< 0.1%
56.26 5
< 0.1%
56.25 6
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct1285
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.995706
Minimum2.32
Maximum20.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:06:00.249982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.32
5-th percentile8.08
Q19.03
median11.08
Q314.12
95-th percentile18.8
Maximum20.66
Range18.34
Interquartile range (IQR)5.09

Descriptive statistics

Standard deviation3.5082389
Coefficient of variation (CV)0.29245789
Kurtosis-0.65372502
Mean11.995706
Median Absolute Deviation (MAD)2.4
Skewness0.74749541
Sum10959217
Variance12.30774
MonotonicityNot monotonic
2025-06-11T00:06:00.500127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.51 4757
 
0.5%
8.52 4514
 
0.5%
8.12 4441
 
0.5%
9.93 4405
 
0.5%
8.11 4178
 
0.5%
9.9 4113
 
0.5%
9.5 4022
 
0.4%
8.51 3619
 
0.4%
8.15 3189
 
0.3%
9.39 3178
 
0.3%
Other values (1275) 873179
95.6%
ValueCountFrequency (%)
2.32 1
 
< 0.1%
7.71 2
 
< 0.1%
7.72 45
< 0.1%
7.73 102
< 0.1%
7.74 99
< 0.1%
7.75 108
< 0.1%
7.76 83
< 0.1%
7.77 83
< 0.1%
7.78 81
< 0.1%
7.79 78
< 0.1%
ValueCountFrequency (%)
20.66 1
 
< 0.1%
20.65 3
< 0.1%
20.64 2
< 0.1%
20.63 2
< 0.1%
20.62 3
< 0.1%
20.61 3
< 0.1%
20.6 2
< 0.1%
20.59 3
< 0.1%
20.58 3
< 0.1%
20.57 3
< 0.1%

speed_over_ground
Real number (ℝ)

Distinct225
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.044391
Minimum0.2
Maximum22.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:06:00.646285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile6.5
Q19.8
median11.9
Q314.6
95-th percentile17.5
Maximum22.6
Range22.4
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation3.5389761
Coefficient of variation (CV)0.29382773
Kurtosis0.58274964
Mean12.044391
Median Absolute Deviation (MAD)2.4
Skewness-0.42493849
Sum11003696
Variance12.524352
MonotonicityNot monotonic
2025-06-11T00:06:00.781189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.4 13193
 
1.4%
10.5 13133
 
1.4%
10.7 12783
 
1.4%
10.6 12764
 
1.4%
10.3 12519
 
1.4%
11 11598
 
1.3%
11.6 11505
 
1.3%
10.9 11485
 
1.3%
10.2 11480
 
1.3%
11.7 11383
 
1.2%
Other values (215) 791752
86.7%
ValueCountFrequency (%)
0.2 442
 
< 0.1%
0.3 1617
0.2%
0.4 1337
0.1%
0.5 1054
0.1%
0.6 958
0.1%
0.7 814
0.1%
0.8 907
0.1%
0.9 843
0.1%
1 771
0.1%
1.1 738
0.1%
ValueCountFrequency (%)
22.6 1
 
< 0.1%
22.5 1
 
< 0.1%
22.4 2
 
< 0.1%
22.3 1
 
< 0.1%
22.2 4
 
< 0.1%
22.1 4
 
< 0.1%
22 6
< 0.1%
21.9 5
< 0.1%
21.8 6
< 0.1%
21.7 12
< 0.1%

course_over_ground
Real number (ℝ)

High correlation 

Distinct3601
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.97899
Minimum0
Maximum360
Zeros192
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:06:00.915365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44
Q181.8
median99.1
Q3134
95-th percentile312
Maximum360
Range360
Interquartile range (IQR)52.2

Descriptive statistics

Standard deviation76.465793
Coefficient of variation (CV)0.61182916
Kurtosis1.3315164
Mean124.97899
Median Absolute Deviation (MAD)21.5
Skewness1.493163
Sum1.1418018 × 108
Variance5847.0174
MonotonicityNot monotonic
2025-06-11T00:06:01.062180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 7475
 
0.8%
90 7334
 
0.8%
82 6782
 
0.7%
86 5624
 
0.6%
89 5526
 
0.6%
85 5337
 
0.6%
83 5150
 
0.6%
87 4993
 
0.5%
88 4950
 
0.5%
100 4679
 
0.5%
Other values (3591) 855745
93.7%
ValueCountFrequency (%)
0 192
< 0.1%
0.1 290
< 0.1%
0.2 26
 
< 0.1%
0.3 26
 
< 0.1%
0.4 19
 
< 0.1%
0.5 37
 
< 0.1%
0.6 31
 
< 0.1%
0.7 29
 
< 0.1%
0.8 23
 
< 0.1%
0.9 44
 
< 0.1%
ValueCountFrequency (%)
360 12
 
< 0.1%
359.9 43
< 0.1%
359.8 29
< 0.1%
359.7 16
 
< 0.1%
359.6 28
< 0.1%
359.5 28
< 0.1%
359.4 20
< 0.1%
359.3 31
< 0.1%
359.2 22
< 0.1%
359.1 40
< 0.1%

true_heading
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.71852
Minimum0
Maximum511
Zeros565
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-11T00:06:01.208809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45
Q182
median99
Q3136
95-th percentile320
Maximum511
Range511
Interquartile range (IQR)54

Descriptive statistics

Standard deviation89.243751
Coefficient of variation (CV)0.68271695
Kurtosis3.8355445
Mean130.71852
Median Absolute Deviation (MAD)22
Skewness1.9151708
Sum1.1942379 × 108
Variance7964.4471
MonotonicityNot monotonic
2025-06-11T00:06:01.353904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 22874
 
2.5%
81 21301
 
2.3%
83 20883
 
2.3%
90 20022
 
2.2%
84 18941
 
2.1%
85 18885
 
2.1%
89 16462
 
1.8%
86 16055
 
1.8%
88 15375
 
1.7%
80 15244
 
1.7%
Other values (351) 727553
79.6%
ValueCountFrequency (%)
0 565
0.1%
1 410
< 0.1%
2 349
< 0.1%
3 363
< 0.1%
4 262
< 0.1%
5 277
< 0.1%
6 271
< 0.1%
7 314
< 0.1%
8 282
< 0.1%
9 205
 
< 0.1%
ValueCountFrequency (%)
511 12963
1.4%
359 399
 
< 0.1%
358 412
 
< 0.1%
357 337
 
< 0.1%
356 395
 
< 0.1%
355 465
 
0.1%
354 408
 
< 0.1%
353 295
 
< 0.1%
352 265
 
< 0.1%
351 223
 
< 0.1%

destination
Categorical

High correlation  Imbalance 

Distinct16
Distinct (%)< 0.1%
Missing68
Missing (%)< 0.1%
Memory size54.9 MiB
DE.HAM
375027 
PL.GDY
291375 
PL.GDN
211838 
LT.KLJ
 
23353
DE.STA
 
3787
Other values (11)
 
8147

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5481162
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDE.HAM
2nd rowDE.HAM
3rd rowDE.HAM
4th rowDE.HAM
5th rowDE.HAM

Common Values

ValueCountFrequency (%)
DE.HAM 375027
41.0%
PL.GDY 291375
31.9%
PL.GDN 211838
23.2%
LT.KLJ 23353
 
2.6%
DE.STA 3787
 
0.4%
DE.KEL 3267
 
0.4%
DE.BRV 2439
 
0.3%
RU.KGD 1337
 
0.1%
SE.NOK 540
 
0.1%
SE.HAD 334
 
< 0.1%
Other values (6) 230
 
< 0.1%

Length

2025-06-11T00:06:01.478678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de.ham 375027
41.1%
pl.gdy 291375
31.9%
pl.gdn 211838
23.2%
lt.klj 23353
 
2.6%
de.sta 3787
 
0.4%
de.kel 3267
 
0.4%
de.brv 2439
 
0.3%
ru.kgd 1337
 
0.1%
se.nok 540
 
0.1%
se.had 334
 
< 0.1%
Other values (6) 230
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 913527
16.7%
D 889526
16.2%
L 553292
10.1%
G 504550
9.2%
P 503319
9.2%
E 388827
7.1%
A 379148
6.9%
H 375363
6.8%
M 375027
6.8%
Y 291375
 
5.3%
Other values (13) 307208
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5481162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 913527
16.7%
D 889526
16.2%
L 553292
10.1%
G 504550
9.2%
P 503319
9.2%
E 388827
7.1%
A 379148
6.9%
H 375363
6.8%
M 375027
6.8%
Y 291375
 
5.3%
Other values (13) 307208
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5481162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 913527
16.7%
D 889526
16.2%
L 553292
10.1%
G 504550
9.2%
P 503319
9.2%
E 388827
7.1%
A 379148
6.9%
H 375363
6.8%
M 375027
6.8%
Y 291375
 
5.3%
Other values (13) 307208
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5481162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 913527
16.7%
D 889526
16.2%
L 553292
10.1%
G 504550
9.2%
P 503319
9.2%
E 388827
7.1%
A 379148
6.9%
H 375363
6.8%
M 375027
6.8%
Y 291375
 
5.3%
Other values (13) 307208
 
5.6%

Interactions

2025-06-11T00:05:50.813658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:21.259824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.489676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:25.764246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:27.899149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:30.107584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:32.301909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:34.651858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:37.068580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:39.263106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:41.587342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:43.858345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:46.151139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.496996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:50.970759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:21.411480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.642080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:25.913721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:28.049065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:30.258259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:32.465283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:34.807100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:37.222763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:39.417024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:41.743048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:44.017959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:46.305609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.659494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:51.131611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:21.569226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.808438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.064138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:28.204116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:30.424935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:32.634973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:34.990486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:37.381391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:39.577356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:41.907167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:44.184270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:46.468679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.825836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:51.284346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:21.714642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.962673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.207045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:28.345203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:30.572027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:32.794738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:35.141967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:37.530864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:39.724915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:42.060162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:44.336230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:46.618887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.987190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:51.443021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:21.869674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:24.119752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.357304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:28.511286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:30.719321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:32.960305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:35.439993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:37.685111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:39.880254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:42.219563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:44.500000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:46.776564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:49.150883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:51.607973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:22.029735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:24.279060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.510312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:28.672885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:30.873274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:33.121301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:35.600036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:37.841300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:40.034877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:42.375874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:44.660560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:46.933208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:49.317607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:51.776885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:22.193859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:24.451945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.670638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:28.837913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.040807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:33.294207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:35.765195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.006943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:40.203592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:42.546026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:44.832588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:47.199964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:49.492316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:51.943950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:22.363286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:24.614426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.824969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.003933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.201967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:33.469071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:35.930654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.163574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:40.362916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:42.726639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.001770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:47.376345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:49.661443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:52.101113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:22.534199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:24.769557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:26.976509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.155523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.353281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:33.632754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:36.088071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.318295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:40.519778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:42.883167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.162966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:47.536694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:49.821720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:52.266212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:22.692596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:24.953307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:27.134950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.319172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.515668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:33.807415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:36.252376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.480844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:40.682192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:43.052748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.330466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:47.699557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:49.992052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:52.424834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:22.850198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:25.133916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:27.285423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.473640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.668541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:33.976780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:36.411808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.635387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:40.841261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:43.212655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.489362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:47.855645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:50.153958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:52.592756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.017339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:25.297010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:27.444875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.638743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.831183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:34.149237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:36.587215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.795860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:41.007189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:43.378048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.657968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.018914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:50.324544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:52.747777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.167584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:25.449013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:27.592476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.789793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:31.979843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:34.310226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:36.742902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:38.948160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:41.263726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:43.536649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.815637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.171891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:50.481147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:52.913292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:23.336778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:25.612421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:27.749770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:29.954458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:32.142415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:34.493539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:36.907923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:39.108701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:41.426029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:43.701637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:45.985592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:48.337928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-06-11T00:05:50.652452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-06-11T00:06:01.568301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
breadthcourse_over_grounddestinationdraughtend_latitudeend_longitudeend_portlatitudelengthlongitudeship_typespeed_over_groundstart_latitudestart_longitudestart_porttrip_idtrue_heading
breadth1.0000.0340.1970.9020.078-0.2720.406-0.0880.937-0.0970.3020.432-0.037-0.1830.406-0.0200.020
course_over_ground0.0341.0000.1670.004-0.290-0.2900.460-0.344-0.005-0.1230.0360.001-0.323-0.3070.4600.0070.937
destination0.1970.1671.0000.1820.6980.9950.9950.3540.2300.3270.1700.1360.4480.7040.9950.1600.155
draught0.9020.0040.1821.0000.120-0.1790.266-0.0190.913-0.0280.3300.4240.067-0.1060.2660.018-0.003
end_latitude0.078-0.2900.6980.1201.0000.5251.0000.7510.1520.736-0.096-0.0200.8150.7981.000-0.025-0.283
end_longitude-0.272-0.2900.995-0.1790.5251.0001.0000.719-0.1560.731-0.037-0.2680.8130.8171.0000.009-0.262
end_port0.4060.4600.9950.2661.0001.0001.0001.0000.3980.9190.2290.2461.0001.0001.0000.2630.397
latitude-0.088-0.3440.354-0.0190.7510.7191.0001.0000.0060.800-0.071-0.0430.8130.7971.000-0.013-0.322
length0.937-0.0050.2300.9130.152-0.1560.3980.0061.000-0.0010.2740.4270.099-0.1000.398-0.049-0.015
longitude-0.097-0.1230.327-0.0280.7360.7310.9190.800-0.0011.000-0.075-0.2110.8140.7980.919-0.013-0.111
ship_type0.3020.0360.1700.330-0.096-0.0370.229-0.0710.274-0.0751.0000.189-0.069-0.0880.2290.1460.036
speed_over_ground0.4320.0010.1360.424-0.020-0.2680.246-0.0430.427-0.2110.1891.000-0.113-0.1910.246-0.058-0.025
start_latitude-0.037-0.3230.4480.0670.8150.8131.0000.8130.0990.814-0.069-0.1131.0000.8421.000-0.012-0.304
start_longitude-0.183-0.3070.704-0.1060.7980.8171.0000.797-0.1000.798-0.088-0.1910.8421.0001.000-0.004-0.286
start_port0.4060.4600.9950.2661.0001.0001.0001.0000.3980.9190.2290.2461.0001.0001.0000.2630.397
trip_id-0.0200.0070.1600.018-0.0250.0090.263-0.013-0.049-0.0130.146-0.058-0.012-0.0040.2631.0000.007
true_heading0.0200.9370.155-0.003-0.283-0.2620.397-0.322-0.015-0.1110.036-0.025-0.304-0.2860.3970.0071.000

Missing values

2025-06-11T00:05:53.212755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-11T00:05:54.264894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-11T00:05:55.913059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

trip_idstart_latitudestart_longitudestart_timeend_latitudeend_longitudeend_timestart_portend_porttime_stampship_typelengthbreadthdraughtlatitudelongitudespeed_over_groundcourse_over_groundtrue_headingdestination
03913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:07:00+00:0071.0277.042.011.5453.578.530.7331.2143DE.HAM
13913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:10:00+00:0071.0277.042.011.5453.578.531.6315.3117DE.HAM
23913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:10:00+00:0071.0277.042.011.5453.578.532.8322.6100DE.HAM
33913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:12:00+00:0071.0277.042.011.5453.578.532.8286.374DE.HAM
43913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:16:00+00:0071.0277.042.011.5453.578.534.3333.1333DE.HAM
53913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:17:00+00:0071.0277.042.011.5453.578.535.2334.0333DE.HAM
63913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:18:00+00:0071.0277.042.011.5453.578.535.7333.0333DE.HAM
73913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:19:00+00:0071.0277.042.011.5453.578.526.3333.0333DE.HAM
83913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:20:00+00:0071.0277.042.011.5453.588.526.8333.0333DE.HAM
93913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:21:00+00:0071.0277.042.011.5453.588.527.1332.1333DE.HAM
trip_idstart_latitudestart_longitudestart_timeend_latitudeend_longitudeend_timestart_portend_porttime_stampship_typelengthbreadthdraughtlatitudelongitudespeed_over_groundcourse_over_groundtrue_headingdestination
913585220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 14:02:00+00:0070.089.013.04.054.5018.747.2222.0215PL.GDN
913586220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 14:01:00+00:0070.089.013.04.054.5018.747.2221.6215PL.GDN
913587220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 14:00:00+00:0070.089.013.04.054.5018.747.2221.5215PL.GDN
913588220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:59:00+00:0070.089.013.04.054.5018.757.2221.2215PL.GDN
913589220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:58:00+00:0070.089.013.04.054.5018.757.2220.8215PL.GDN
913590220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:57:00+00:0070.089.013.04.054.5118.757.2221.0215PL.GDN
913591220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:56:00+00:0070.089.013.04.054.5118.757.2221.9215PL.GDN
913592220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:55:00+00:0070.089.013.04.054.5118.757.2222.1215PL.GDN
913593220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:54:00+00:0070.089.013.04.054.5118.767.2221.2215PL.GDN
913594220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:53:00+00:0070.089.013.04.054.5118.767.2221.0214PL.GDN